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- Image captioning with visual attention is a task of generating natural language descriptions of images by focusing on the most relevant regions123. It involves using a neural encoder-decoder framework that learns to attend to different parts of the image while producing words23. Some methods also use user attention to incorporate user-contributed tags for social image captioning4. The performance of image captioning with visual attention is evaluated by metrics such as BLEU, METEOR, and CIDEr12.Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.
Image Caption Generation with Visual Attention
- Introduction Captioning involves automatically generating natural language descriptions of objects present in the image and their relationships with the environment. ...
medium.com/swlh/image-caption-generation-with-v…Image captioning with visual attention
- Setup apt install --allow-change-held-packages libcudnn8=8.6.0.163-1+cuda11.8 ...
- [Optional] Data handling This section downloads a captions dataset and prepares it for training. ...
www.tensorflow.org/text/tutorials/image_captioningAttention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as "the" and "of".arxiv.org/abs/1612.01887In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously.Visual attention is used to compress a large mount of salient visual information, while user attention is applied to adjust the description of the social images with user-contributed tags.www.mdpi.com/1424-8220/18/2/646 - People also ask
WEBMay 31, 2024 · The model architecture used here is inspired by Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, but has been updated to use a 2-layer Transformer-decoder.
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